These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

137 related articles for article (PubMed ID: 36572323)

  • 1. Differentiation of Pilocytic Astrocytoma from Glioblastoma using a Machine-Learning framework based upon quantitative T1 perfusion MRI.
    Vats N; Sengupta A; Gupta RK; Patir R; Vaishya S; Ahlawat S; Saini J; Agarwal S; Singh A
    Magn Reson Imaging; 2023 May; 98():76-82. PubMed ID: 36572323
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Glioma grading using a machine-learning framework based on optimized features obtained from T
    Sengupta A; Ramaniharan AK; Gupta RK; Agarwal S; Singh A
    J Magn Reson Imaging; 2019 Oct; 50(4):1295-1306. PubMed ID: 30895704
    [TBL] [Abstract][Full Text] [Related]  

  • 3. On differentiation between vasogenic edema and non-enhancing tumor in high-grade glioma patients using a support vector machine classifier based upon pre and post-surgery MRI images.
    Sengupta A; Agarwal S; Gupta PK; Ahlawat S; Patir R; Gupta RK; Singh A
    Eur J Radiol; 2018 Sep; 106():199-208. PubMed ID: 30150045
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Radiomics-based evaluation and possible characterization of dynamic contrast enhanced (DCE) perfusion derived different sub-regions of Glioblastoma.
    Suhail Parvaze ; Bhattacharjee R; Singh A; Ahlawat S; Patir R; Vaishya S; Shah TJ; Gupta RK
    Eur J Radiol; 2023 Feb; 159():110655. PubMed ID: 36577183
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Comparative evaluation of intracranial oligodendroglioma and astrocytoma of similar grades using conventional and T1-weighted DCE-MRI.
    Gupta M; Gupta A; Yadav V; Parvaze SP; Singh A; Saini J; Patir R; Vaishya S; Ahlawat S; Gupta RK
    Neuroradiology; 2021 Aug; 63(8):1227-1239. PubMed ID: 33469693
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Differentiation between glioblastoma, brain metastasis and subtypes using radiomics analysis.
    Artzi M; Bressler I; Ben Bashat D
    J Magn Reson Imaging; 2019 Aug; 50(2):519-528. PubMed ID: 30635952
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Differentiation of pilocytic and pilomyxoid astrocytomas using dynamic susceptibility contrast perfusion and diffusion weighted imaging.
    Ho CY; Supakul N; Patel PU; Seit V; Groswald M; Cardinal J; Lin C; Kralik SF
    Neuroradiology; 2020 Jan; 62(1):81-88. PubMed ID: 31676961
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Application of MR morphologic, diffusion tensor, and perfusion imaging in the classification of brain tumors using machine learning scheme.
    Shrot S; Salhov M; Dvorski N; Konen E; Averbuch A; Hoffmann C
    Neuroradiology; 2019 Jul; 61(7):757-765. PubMed ID: 30949746
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Quantification of Radiomics features of Peritumoral Vasogenic Edema extracted from fluid-attenuated inversion recovery images in glioblastoma and isolated brain metastasis, using T1-dynamic contrast-enhanced Perfusion analysis.
    Parvaze PS; Bhattacharjee R; Verma YK; Singh RK; Yadav V; Singh A; Khanna G; Ahlawat S; Trivedi R; Patir R; Vaishya S; Shah TJ; Gupta RK
    NMR Biomed; 2023 May; 36(5):e4884. PubMed ID: 36453877
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Differentiation between pilocytic astrocytoma and glioblastoma: a decision tree model using contrast-enhanced magnetic resonance imaging-derived quantitative radiomic features.
    Dong F; Li Q; Xu D; Xiu W; Zeng Q; Zhu X; Xu F; Jiang B; Zhang M
    Eur Radiol; 2019 Aug; 29(8):3968-3975. PubMed ID: 30421019
    [TBL] [Abstract][Full Text] [Related]  

  • 11. DSC-PWI presurgical differentiation of grade 4 astrocytoma and glioblastoma in young adults: rCBV percentile analysis across enhancing and non-enhancing regions.
    Pons-Escoda A; Naval-Baudin P; Viveros M; Flores-Casaperalta S; Martinez-Zalacaín I; Plans G; Vidal N; Cos M; Majos C
    Neuroradiology; 2024 Aug; 66(8):1267-1277. PubMed ID: 38834877
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Radiomics features to distinguish glioblastoma from primary central nervous system lymphoma on multi-parametric MRI.
    Kim Y; Cho HH; Kim ST; Park H; Nam D; Kong DS
    Neuroradiology; 2018 Dec; 60(12):1297-1305. PubMed ID: 30232517
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Primary central nervous system lymphoma and atypical glioblastoma: Differentiation using radiomics approach.
    Suh HB; Choi YS; Bae S; Ahn SS; Chang JH; Kang SG; Kim EH; Kim SH; Lee SK
    Eur Radiol; 2018 Sep; 28(9):3832-3839. PubMed ID: 29626238
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Machine learning-based radiomic evaluation of treatment response prediction in glioblastoma.
    Patel M; Zhan J; Natarajan K; Flintham R; Davies N; Sanghera P; Grist J; Duddalwar V; Peet A; Sawlani V
    Clin Radiol; 2021 Aug; 76(8):628.e17-628.e27. PubMed ID: 33941364
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Multiparametric imaging-based differentiation of lymphoma and glioblastoma: using T1-perfusion, diffusion, and susceptibility-weighted MRI.
    Saini J; Kumar Gupta P; Awasthi A; Pandey CM; Singh A; Patir R; Ahlawat S; Sadashiva N; Mahadevan A; Kumar Gupta R
    Clin Radiol; 2018 Nov; 73(11):986.e7-986.e15. PubMed ID: 30197047
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Machine learning based on multi-parametric magnetic resonance imaging to differentiate glioblastoma multiforme from primary cerebral nervous system lymphoma.
    Nakagawa M; Nakaura T; Namimoto T; Kitajima M; Uetani H; Tateishi M; Oda S; Utsunomiya D; Makino K; Nakamura H; Mukasa A; Hirai T; Yamashita Y
    Eur J Radiol; 2018 Nov; 108():147-154. PubMed ID: 30396648
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Differentiation of Pseudoprogression from True Progressionin Glioblastoma Patients after Standard Treatment: A Machine Learning Strategy Combinedwith Radiomics Features from T
    Sun YZ; Yan LF; Han Y; Nan HY; Xiao G; Tian Q; Pu WH; Li ZY; Wei XC; Wang W; Cui GB
    BMC Med Imaging; 2021 Feb; 21(1):17. PubMed ID: 33535988
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Diagnostic Accuracy of T1-Weighted Dynamic Contrast-Enhanced-MRI and DWI-ADC for Differentiation of Glioblastoma and Primary CNS Lymphoma.
    Lin X; Lee M; Buck O; Woo KM; Zhang Z; Hatzoglou V; Omuro A; Arevalo-Perez J; Thomas AA; Huse J; Peck K; Holodny AI; Young RJ
    AJNR Am J Neuroradiol; 2017 Mar; 38(3):485-491. PubMed ID: 27932505
    [TBL] [Abstract][Full Text] [Related]  

  • 19. An initial experience of machine learning based on multi-sequence texture parameters in magnetic resonance imaging to differentiate glioblastoma from brain metastases.
    Tateishi M; Nakaura T; Kitajima M; Uetani H; Nakagawa M; Inoue T; Kuroda JI; Mukasa A; Yamashita Y
    J Neurol Sci; 2020 Mar; 410():116514. PubMed ID: 31869660
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Machine Learning-based Texture Analysis of Contrast-enhanced MR Imaging to Differentiate between Glioblastoma and Primary Central Nervous System Lymphoma.
    Kunimatsu A; Kunimatsu N; Yasaka K; Akai H; Kamiya K; Watadani T; Mori H; Abe O
    Magn Reson Med Sci; 2019 Jan; 18(1):44-52. PubMed ID: 29769456
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 7.